딥러닝을 이용한 포트홀 검출 시스템

Deep Learning-based Pothole Detection System

  • 황성진 (상명대학교 소프트웨어학과) ;
  • 홍석우 (상명대학교 소프트웨어학과) ;
  • 윤종서 (상명대학교 소프트웨어학과) ;
  • 박희민 (상명대학교 소프트웨어학과) ;
  • 김현철 (상명대학교 소프트웨어학과)
  • 투고 : 2021.03.12
  • 심사 : 2021.03.17
  • 발행 : 2021.03.31

초록

The automotive industry is developing day by day. Among them, it is very important to prevent accidents while driving. However, despite the importance of developing automobile industry technology, accidents due to road defects increase every year, especially in the rainy season. To this end, we proposed a road defect detection system for road management by converging deep learning and raspberry pi, which show various possibilities. In this paper, we developed a system that visually displays through a map after analyzing the images captured by the Raspberry Pi and the route GPS. The deep learning model trained for this system achieved 96% accuracy. Through this system, it is expected to manage road defects efficiently at a low cost.

키워드

참고문헌

  1. IWET News. [online]. Available: http://www.iwet.co.kr/news/articleView.html?idxno=46186.
  2. Raspberry Pi. [online]. Available: https://www.raspberrypi.org/.
  3. Redmon, J., and Farhadi, A., "Yolov3: An incremental improvement," arXiv preprint arXiv:1804.02767, 2018.
  4. Zhang, L, Yang, F., Zhang, Y., and Zhu, Y., "Road crack detection using deep convolutional neural network", IEEE International Conference on Image Processing, 2016.
  5. Picamera. [online]. Available: https://picamera.readthedocs.io/en/release-1.13/.
  6. OpenCV. [online]. Available: https://opencv.org/.
  7. Folium. [online]. Available: https://python-visualization.github.io/folium/.
  8. GoogleAPI. [online]. Available: https://support.google.com/webmasters/answer/1061943?hl=ko.
  9. Selenium. [online]. Available: https://www.selenium.dev/.
  10. AlexeyAB Yolo_mark. [online]. Available: https://github.com/AlexeyAB/Yolo_mark.
  11. PyQt. [online]. Available: http://www.riverbankcomputing.com/software/pyqt/.